When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generation
Large language models (LLMs) have shown strong performance in text generation tasks, yet their effectiveness on headline generation remains sensitive to how input context is selected and presented. In this work, we investigate Bengali news headline generation as a document-level generation task that requires effective selection and presentation of salient contextual information from long-form articles. Using Gemini-2.0-Flash, Llama-3.3-70B, and GPT-4o, we systematically study the effects of context selection, prompting strategies, and in-context learning (i.e., few-shot) on the quality of headline generation. Our experiments show that providing the full article does not necessarily improv...
| Authors | Muhammad Ashad Kabir, Kawsar Ahmed, Md. Osama |
|---|---|
| Primary category | cs.CL |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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